Paper: Seed and Grow: Augmenting Statistically Generated Summary Sentences using Schematic Word Patterns

ACL ID D08-1057
Title Seed and Grow: Augmenting Statistically Generated Summary Sentences using Schematic Word Patterns
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

We examine the problem of content selection in statistical novel sentence generation. Our approach models the processes performed by professional editors when incorporating ma- terial from additional sentences to support some initially chosen key summary sentence, a process we refer to as Sentence Augmen- tation. We propose and evaluate a method called “Seed and Grow” for selecting such auxiliary information. Additionally, we argue that this can be performed using schemata, as represented by word-pair co-occurrences, and demonstrate its use in statistical summary sen- tence generation. Evaluation results are sup- portive, indicating that a schemata model sig- nificantly improves over the baseline.